Overview

Dataset statistics

Number of variables9
Number of observations10703
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory752.7 KiB
Average record size in memory72.0 B

Variable types

NUM9

Warnings

mean_closeness is highly correlated with average_clustering and 1 other fieldsHigh correlation
average_clustering is highly correlated with mean_closeness and 1 other fieldsHigh correlation
assortativity is highly correlated with average_clustering and 1 other fieldsHigh correlation
rg has unique values Unique
shape has unique values Unique

Reproduction

Analysis started2020-11-25 20:12:01.860857
Analysis finished2020-11-25 20:12:13.668312
Duration11.81 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

rg
Real number (ℝ≥0)

UNIQUE

Distinct10703
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4006523501
Minimum0.2647429215
Maximum0.5000658117
Zeros0
Zeros (%)0.0%
Memory size83.6 KiB
2020-11-25T14:12:13.761162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.2647429215
5-th percentile0.314951375
Q10.3756062709
median0.4038566037
Q30.4323803498
95-th percentile0.4666286933
Maximum0.5000658117
Range0.2353228901
Interquartile range (IQR)0.05677407881

Descriptive statistics

Standard deviation0.04535279828
Coefficient of variation (CV)0.1131973849
Kurtosis-0.3184258063
Mean0.4006523501
Median Absolute Deviation (MAD)0.02828587499
Skewness-0.3792377219
Sum4288.182103
Variance0.002056876312
MonotocityNot monotonic
2020-11-25T14:12:13.902378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.44694835281< 0.1%
 
0.41028863141< 0.1%
 
0.40060587441< 0.1%
 
0.41850647871< 0.1%
 
0.4094141511< 0.1%
 
0.36184362181< 0.1%
 
0.34268488751< 0.1%
 
0.45069408291< 0.1%
 
0.3925480111< 0.1%
 
0.43323178891< 0.1%
 
Other values (10693)1069399.9%
 
ValueCountFrequency (%) 
0.26474292151< 0.1%
 
0.26630381851< 0.1%
 
0.26689799121< 0.1%
 
0.26742963611< 0.1%
 
0.26744080811< 0.1%
 
ValueCountFrequency (%) 
0.50006581171< 0.1%
 
0.49914412791< 0.1%
 
0.49913883091< 0.1%
 
0.49904858471< 0.1%
 
0.49887783461< 0.1%
 

mean_displacement
Real number (ℝ≥0)

Distinct10648
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.38374133
Minimum0
Maximum142.2368066
Zeros56
Zeros (%)0.5%
Memory size83.6 KiB
2020-11-25T14:12:14.026416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.278279263
Q16.787500261
median9.66576856
Q320.0354263
95-th percentile39.12502738
Maximum142.2368066
Range142.2368066
Interquartile range (IQR)13.24792604

Descriptive statistics

Standard deviation11.59685577
Coefficient of variation (CV)0.8062475197
Kurtosis5.940268228
Mean14.38374133
Median Absolute Deviation (MAD)3.97054806
Skewness1.774379636
Sum153949.1835
Variance134.4870638
MonotocityNot monotonic
2020-11-25T14:12:14.145184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0560.5%
 
1.1344076241< 0.1%
 
15.120614011< 0.1%
 
8.2084159851< 0.1%
 
5.9789498961< 0.1%
 
8.0770995071< 0.1%
 
17.829126031< 0.1%
 
11.103103771< 0.1%
 
9.2504495121< 0.1%
 
33.072644641< 0.1%
 
Other values (10638)1063899.4%
 
ValueCountFrequency (%) 
0560.5%
 
1.0166660451< 0.1%
 
1.0239161761< 0.1%
 
1.0352389291< 0.1%
 
1.0353953941< 0.1%
 
ValueCountFrequency (%) 
142.23680661< 0.1%
 
119.47645421< 0.1%
 
117.66914261< 0.1%
 
115.45183611< 0.1%
 
105.97171721< 0.1%
 

shape
Real number (ℝ)

UNIQUE

Distinct10703
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003499573175
Minimum-0.06826915628
Maximum0.1415545417
Zeros0
Zeros (%)0.0%
Memory size83.6 KiB
2020-11-25T14:12:14.278190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.06826915628
5-th percentile-0.006057928124
Q1-0.001127639932
median0.0004272408729
Q30.003713336254
95-th percentile0.02850722117
Maximum0.1415545417
Range0.2098236979
Interquartile range (IQR)0.004840976186

Descriptive statistics

Standard deviation0.01361309761
Coefficient of variation (CV)3.889930837
Kurtosis18.37578008
Mean0.003499573175
Median Absolute Deviation (MAD)0.002166574374
Skewness3.234879993
Sum37.45593169
Variance0.0001853164265
MonotocityNot monotonic
2020-11-25T14:12:14.396523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.00012910514051< 0.1%
 
0.00066013098551< 0.1%
 
0.0002701783241< 0.1%
 
-0.00010548374041< 0.1%
 
0.0027495659881< 0.1%
 
-0.0022627963141< 0.1%
 
0.0020931247821< 0.1%
 
0.0018359382831< 0.1%
 
-0.00023751662271< 0.1%
 
0.012043813021< 0.1%
 
Other values (10693)1069399.9%
 
ValueCountFrequency (%) 
-0.068269156281< 0.1%
 
-0.063639069681< 0.1%
 
-0.057552284971< 0.1%
 
-0.055199341571< 0.1%
 
-0.052703460231< 0.1%
 
ValueCountFrequency (%) 
0.14155454171< 0.1%
 
0.13095402031< 0.1%
 
0.12481024481< 0.1%
 
0.12270424891< 0.1%
 
0.12074233921< 0.1%
 

density
Real number (ℝ≥0)

Distinct7843
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001448799739
Minimum0.00134478223
Maximum0.001676962743
Zeros0
Zeros (%)0.0%
Memory size83.6 KiB
2020-11-25T14:12:14.517539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.00134478223
5-th percentile0.001365176148
Q10.001407108047
median0.001453081545
Q30.001486241778
95-th percentile0.001532550889
Maximum0.001676962743
Range0.0003321805127
Interquartile range (IQR)7.913373087e-05

Descriptive statistics

Standard deviation5.285368923e-05
Coefficient of variation (CV)0.03648101791
Kurtosis-0.5889009284
Mean0.001448799739
Median Absolute Deviation (MAD)3.784919995e-05
Skewness0.05820580056
Sum15.50650361
Variance2.793512465e-09
MonotocityNot monotonic
2020-11-25T14:12:14.638378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.00135939786390.1%
 
0.00135988365970.1%
 
0.0014790407160.1%
 
0.00137965865360.1%
 
0.00147368164560.1%
 
0.00145348623260.1%
 
0.00135745992160.1%
 
0.0014335812860.1%
 
0.00145066959960.1%
 
0.00136533633360.1%
 
Other values (7833)1063999.4%
 
ValueCountFrequency (%) 
0.001344782231< 0.1%
 
0.0013462923731< 0.1%
 
0.0013488657271< 0.1%
 
0.0013490153541< 0.1%
 
0.0013491405871< 0.1%
 
ValueCountFrequency (%) 
0.0016769627431< 0.1%
 
0.0016336529611< 0.1%
 
0.0016296851571< 0.1%
 
0.0016285878221< 0.1%
 
0.0016278070591< 0.1%
 

clique_number
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.050920303
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Memory size83.6 KiB
2020-11-25T14:12:14.746062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q13
median4
Q34
95-th percentile6
Maximum8
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9969702016
Coefficient of variation (CV)0.2461095571
Kurtosis1.581797935
Mean4.050920303
Median Absolute Deviation (MAD)0
Skewness1.258944004
Sum43357
Variance0.9939495828
MonotocityNot monotonic
2020-11-25T14:12:14.823489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
4545451.0%
 
3309828.9%
 
510569.9%
 
66966.5%
 
73713.5%
 
8230.2%
 
25< 0.1%
 
ValueCountFrequency (%) 
25< 0.1%
 
3309828.9%
 
4545451.0%
 
510569.9%
 
66966.5%
 
ValueCountFrequency (%) 
8230.2%
 
73713.5%
 
66966.5%
 
510569.9%
 
4545451.0%
 

average_clustering
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9953
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1389279499
Minimum0
Maximum0.6084742648
Zeros5
Zeros (%)< 0.1%
Memory size83.6 KiB
2020-11-25T14:12:14.931705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02934740472
Q10.04626148262
median0.1060590886
Q30.147800557
95-th percentile0.5987907544
Maximum0.6084742648
Range0.6084742648
Interquartile range (IQR)0.1015390743

Descriptive statistics

Standard deviation0.1558802813
Coefficient of variation (CV)1.122022468
Kurtosis3.973982079
Mean0.1389279499
Median Absolute Deviation (MAD)0.05666858842
Skewness2.251427583
Sum1486.945848
Variance0.0242986621
MonotocityNot monotonic
2020-11-25T14:12:15.051615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.04879074658100.1%
 
0.04424778761100.1%
 
0.0462025316590.1%
 
0.044331855690.1%
 
0.0442757748380.1%
 
0.0488523899870.1%
 
0.0443037974770.1%
 
0.048339638570.1%
 
0.044359949370.1%
 
0.0458676812560.1%
 
Other values (9943)1062399.3%
 
ValueCountFrequency (%) 
05< 0.1%
 
0.0014306151651< 0.1%
 
0.0014323715981< 0.1%
 
0.0014358974361< 0.1%
 
0.0014358974361< 0.1%
 
ValueCountFrequency (%) 
0.60847426481< 0.1%
 
0.60812317881< 0.1%
 
0.60706304981< 0.1%
 
0.60698140951< 0.1%
 
0.60678243621< 0.1%
 

mean_closeness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10700
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0005421201513
Minimum0.000100637931
Maximum0.003373289354
Zeros0
Zeros (%)0.0%
Memory size83.6 KiB
2020-11-25T14:12:15.179904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.000100637931
5-th percentile0.0001251999322
Q10.0001707270123
median0.0005412433383
Q30.000616050486
95-th percentile0.001927778932
Maximum0.003373289354
Range0.003272651422
Interquartile range (IQR)0.0004453234737

Descriptive statistics

Standard deviation0.0005235240156
Coefficient of variation (CV)0.9656973909
Kurtosis4.430790748
Mean0.0005421201513
Median Absolute Deviation (MAD)0.0003022152961
Skewness2.124331053
Sum5.802311979
Variance2.740773949e-07
MonotocityNot monotonic
2020-11-25T14:12:15.303058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.00010257633842< 0.1%
 
0.00010643490322< 0.1%
 
0.00010505724572< 0.1%
 
0.00012094488351< 0.1%
 
0.00065262777441< 0.1%
 
0.00013577156621< 0.1%
 
0.00026965132331< 0.1%
 
0.00013682346791< 0.1%
 
0.00022462093631< 0.1%
 
0.00061644300431< 0.1%
 
Other values (10690)1069099.9%
 
ValueCountFrequency (%) 
0.0001006379311< 0.1%
 
0.00010243978231< 0.1%
 
0.00010257633842< 0.1%
 
0.000102905461< 0.1%
 
0.00010297856691< 0.1%
 
ValueCountFrequency (%) 
0.0033732893541< 0.1%
 
0.0032585712171< 0.1%
 
0.0032486038731< 0.1%
 
0.0032125540941< 0.1%
 
0.003178178791< 0.1%
 

mean_betweenness
Real number (ℝ≥0)

Distinct9627
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.314002446e-05
Minimum4.984923249e-09
Maximum0.00367050182
Zeros0
Zeros (%)0.0%
Memory size83.6 KiB
2020-11-25T14:12:15.435356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.984923249e-09
5-th percentile1.481697435e-08
Q13.839167337e-08
median3.803423336e-07
Q36.611311975e-07
95-th percentile0.0002338628676
Maximum0.00367050182
Range0.003670496835
Interquartile range (IQR)6.227395241e-07

Descriptive statistics

Standard deviation0.0001597285624
Coefficient of variation (CV)4.819808225
Kurtosis156.1037236
Mean3.314002446e-05
Median Absolute Deviation (MAD)3.355512214e-07
Skewness10.67970433
Sum0.3546976818
Variance2.551321366e-08
MonotocityNot monotonic
2020-11-25T14:12:15.558452image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.621746929e-0860.1%
 
1.946777186e-0860.1%
 
2.05458807e-0860.1%
 
1.521178447e-0860.1%
 
1.366464407e-0860.1%
 
2.363943726e-0860.1%
 
1.583883465e-085< 0.1%
 
3.099857352e-085< 0.1%
 
2.028247514e-085< 0.1%
 
3.140337018e-085< 0.1%
 
Other values (9617)1064799.5%
 
ValueCountFrequency (%) 
4.984923249e-091< 0.1%
 
5.032278139e-093< 0.1%
 
5.041821018e-092< 0.1%
 
5.051388041e-092< 0.1%
 
5.079956941e-091< 0.1%
 
ValueCountFrequency (%) 
0.003670501821< 0.1%
 
0.0032190418721< 0.1%
 
0.0032092957341< 0.1%
 
0.0028187808161< 0.1%
 
0.0027895642681< 0.1%
 

assortativity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10701
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8446850952
Minimum0.4461152309
Maximum0.9899011033
Zeros0
Zeros (%)0.0%
Memory size83.6 KiB
2020-11-25T14:12:15.873467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.4461152309
5-th percentile0.5121904605
Q10.8100064214
median0.8328714993
Q30.944246909
95-th percentile0.9732624376
Maximum0.9899011033
Range0.5437858724
Interquartile range (IQR)0.1342404876

Descriptive statistics

Standard deviation0.1262026975
Coefficient of variation (CV)0.1494079844
Kurtosis1.939443223
Mean0.8446850952
Median Absolute Deviation (MAD)0.07910495679
Skewness-1.51542757
Sum9040.664574
Variance0.01592712086
MonotocityNot monotonic
2020-11-25T14:12:16.005791image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.97859653062< 0.1%
 
0.98726706892< 0.1%
 
0.94541075331< 0.1%
 
0.91202401581< 0.1%
 
0.80569725211< 0.1%
 
0.81510637141< 0.1%
 
0.81766902681< 0.1%
 
0.80478179571< 0.1%
 
0.83371560291< 0.1%
 
0.89750358971< 0.1%
 
Other values (10691)1069199.9%
 
ValueCountFrequency (%) 
0.44611523091< 0.1%
 
0.45404553531< 0.1%
 
0.45534036861< 0.1%
 
0.45882845831< 0.1%
 
0.45916163461< 0.1%
 
ValueCountFrequency (%) 
0.98990110331< 0.1%
 
0.98969331631< 0.1%
 
0.98968683891< 0.1%
 
0.98965904981< 0.1%
 
0.98953398331< 0.1%
 

Interactions

2020-11-25T14:12:04.050426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:04.177978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:04.296668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:04.393155image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:04.498331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:04.598109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:04.698655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:04.804695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:04.936559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:05.134142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:05.244765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:05.373884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:05.478805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:05.591881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:05.703166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:05.818656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:05.932922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:06.047926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:06.155590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:06.248501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:06.349772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:06.442924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:06.542827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:06.653229image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:06.767481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:06.874341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:06.977679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:07.073472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:07.177044image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:07.287536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:07.390288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:07.499858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:07.607025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:07.716228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:07.830472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:07.944604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:08.050461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:08.149729image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:08.365387image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:08.466114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:08.573358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:08.677657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:08.783389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:08.893324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:09.003299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:09.105793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:09.206529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:09.315393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:09.416948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:09.525389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:09.630767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:09.737562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:09.848557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:09.959869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:10.063813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:10.169757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:10.283786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:10.390059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:10.504392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:10.615025image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:10.727002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:10.842977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:10.959235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:11.068376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:11.174585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:11.300896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:11.421585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:11.535431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:11.646026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:11.758874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:11.875449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:11.991691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:12.109832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:12.346425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:12.468711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:12.600922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:12.742232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:12.867274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:12.971158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:13.078854image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:13.186642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-11-25T14:12:16.117381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-25T14:12:16.268491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-25T14:12:16.419109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-25T14:12:16.570549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-25T14:12:13.388006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-25T14:12:13.580345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

rgmean_displacementshapedensityclique_numberaverage_clusteringmean_closenessmean_betweennessassortativity
00.4762490.0000000.0012470.00136030.0014360.0001392.054588e-080.892341
10.44391555.1402680.0014910.00138230.0097980.0001713.911007e-080.903483
20.42995350.2914210.0007810.00139230.0034720.0001867.283252e-080.893616
30.41341349.5429630.0021830.00138840.0088210.0001725.556727e-080.924630
40.39652552.880606-0.0003840.00140440.0158290.0002029.449790e-080.902335
50.39055949.946156-0.0010400.00139830.0157560.0001928.436034e-080.905063
60.38755647.897362-0.0017610.00139140.0190770.0001765.043080e-080.915935
70.37776445.165687-0.0036080.00141050.0191090.0002015.126138e-080.923450
80.37187142.052728-0.0056460.00140630.0256990.0002016.809754e-080.898583
90.36841744.360144-0.0061610.00139630.0248770.0001835.665358e-080.923805

Last rows

rgmean_displacementshapedensityclique_numberaverage_clusteringmean_closenessmean_betweennessassortativity
106930.3845636.480627-0.0017030.00146440.1191470.0005623.589257e-070.807784
106940.3840866.376798-0.0015990.00145940.1218360.0005573.428125e-070.817159
106950.3842005.918025-0.0013490.00145540.1166250.0005543.410380e-070.812662
106960.3838005.755412-0.0009560.00146340.1208850.0005693.477754e-070.808373
106970.3834526.059833-0.0007940.00146140.1253260.0005613.351202e-070.810435
106980.3815316.430485-0.0010450.00146540.1209330.0005583.183952e-070.815143
106990.3824095.566823-0.0011610.00144940.1190050.0005412.914492e-070.808140
107000.3821247.064615-0.0012670.00146440.1303690.0005663.166979e-070.813121
107010.3821526.479632-0.0008030.00146340.1194110.0005563.332482e-070.819382
107020.3812726.034650-0.0009050.00146140.1180660.0005573.243762e-070.810699